Transforming Pharmacovigilance Through Artificial Intelligence: Current Trends and Future Perspectives

Abstract

Pharmacovigilance plays a crucial role in detecting, assessing, understanding, and preventing adverse drug reactions throughout the life cycle of medicines. However, the increasing volume, complexity, and diversity of safety data have created significant limitations for conventional, mainly manual and reactive pharmacovigilance systems. Artificial intelligence (AI) provides an opportunity to transform pharmacovigilance into a more efficient, continuous, predictive, and patient-centric system. This review explores the current applications, emerging technologies, challenges, and future perspectives of AI-driven pharmacovigilance. Machine learning, deep learning, natural language processing, data mining, knowledge graphs, robotic process automation, computer vision, large language models, and generative AI can support several pharmacovigilance activities, including individual case safety report processing, case coding and classification, duplicate detection, adverse drug reaction identification, signal detection, causality assessment, predictive risk assessment, and risk–benefit evaluation. AI can reduce repetitive workload, improve processing efficiency, identify patterns within large and unstructured datasets, and support earlier detection of potential safety signals. Emerging approaches such as explainable AI, multimodal AI, federated learning, digital twins, and human–AI collaboration may further improve transparency, privacy, prediction, and individualized safety monitoring. Nevertheless, challenges related to data quality, heterogeneity, algorithmic bias, interoperability, validation, explainability, privacy, cybersecurity, infrastructure, cost, and regulatory requirements remain important barriers. Successful implementation therefore requires robust data infrastructure, validated and interpretable models, appropriate governance, regulatory alignment, continuous monitoring, and skilled pharmacovigilance professionals. Overall, AI is expected to augment rather than replace human expertise and may enable earlier, more personalized, and proactive drug safety decision-making.